Prashanth Chandran

I am a research scientist at Google. I enjoy working on creative applications at the intersection of computer vision, graphics, and machine learning.

I was previously at Disney Research|Studios, Switzerland, and a part of the Facial VFX group.

I completed my Ph.D. at the Computer Graphics Lab at ETH Zurich and Disney Research|Studios, advised by Prof. Markus Gross and co-supervised by Dr. Derek Bradley. Prior to my doctoral studies, I received my M.Sc. in Electrical Engineering & Information Technology from ETH Zurich and my B.E. in Electronics & Communication Engineering from the Madras Institute of Technology, followed by 3 years at Caterpillar Inc. as an embedded electronics engineer.

Recent Publications

Featured Shape modeling Generative models

GNM Head: A Generative aNthropometric Model of the human head

arXiv (2026)

Parametric models of the human head are essential tools in computer vision, graphics, and generative AI. We introduce the Generative aNthropometric Model (GNM), a comprehensive 3D head model...

Details Paper arXiv Project Code BibTeX @article{ploumpis2026gnmhead, title={GNM Head: A Generative aNthropometric Model of the human head}, author={Ploumpis, S. and Bednarik, J. and Zoss, G. and Guseinov, R. and Prasso, L. and Chandran, P. and Boyne, O. and Choutas, V. and Bolkart, T. and Wang, D. and Chai, M. and Qiu, D. and Winberg, S. and Rainer, G. and Bridgeman, L. and Vicini, D. and Riviere, J. and Boetzel, Y. and Koumis, A. and Busch, J. and Herrera, C. and Still, J. and Ysebert, S. and Lincoln, P. and Escolano, S. O. and Rhemann, C. and Wood, E. and Beeler, T. and Zafeiriou, S.}, year={2026}, eprint={2607.23687}, archivePrefix={arXiv}, url={https://arxiv.org/abs/2607.23687}, }

Face capture & animation

Topologically Consistent Multi-view 3D Head Reconstruction via Coarse-Guided Layered Surface Sampling

SIGGRAPH (2026)

From calibrated multi-view images, SHELLS reconstructs 18k-vertex 3D heads in 0.08 seconds. It aggregates DinoV2 features via projective surface-aware feature sampling, allowing a transformer to predict dense semantic...

Details Paper arXiv Project BibTeX @inproceedings{Bolkart2026SHELLS, author = {Bolkart, Timo and Wang, Daoye and Chandran, Prashanth}, title = {Topologically Consistent Multi-view 3D Head Reconstruction via Coarse-Guided Layered Surface Sampling}, year = {2026}, publisher = {Association for Computing Machinery}, keywords = {Registration, 3D Head Reconstruction}, series = {SIGGRAPH Conference Papers '26} }

Featured Shape modeling Face capture & animation Neural representations

Representing 3D Faces with Learnable B-spline Volumes

Computer Vision and Pattern Recognition (CVPR) (2026)

We present CUBE (Control-based Unified B-spline Encoding), a new geometric representation for human faces that combines B-spline volumes with learned features, and demonstrate its use as a decoder...

Details Paper arXiv Project BibTeX @inproceedings{chandran26a, title={Representing 3D Faces with Learnable B-Spline Volumes}, author={Chandran, Prashanth and Wang, Daoye and Bolkart, Timo}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year={2026} }

Generative models

Multimodal Conditional 3D Face Geometry Generation

Shape Modeling International (2025)

In this work, we present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number...

Details arXiv Project BibTeX @article{Otto2025, title = {Multimodal Conditional 3D Face Geometry Generation}, journal = {Computers & Graphics}, volume = {132}, pages = {104325}, year = {2025}, issn = {0097-8493}, author = {Christopher Otto and Prashanth Chandran and Sebastian Weiss and Markus Gross and Gaspard Zoss and Derek Bradley}, }

Face capture & animation Neural rendering

Joint Learning of Depth and Appearance for Portrait Images

Workshop on Human-Interactive Generation and Editing (2025)

In this work, we propose to jointly learn the visual appearance and depth of faces simultaneously in a diffusion-based portrait image generator. Our method embraces the end-to-end diffusion...

Details arXiv Project BibTeX @misc{ji2025jointlearningdepthappearance, title={Joint Learning of Depth and Appearance for Portrait Image Animation}, author={Xinya Ji and Gaspard Zoss and Prashanth Chandran and Lingchen Yang and Xun Cao and Barbara Solenthaler and Derek Bradley}, year={2025}, eprint={2501.08649}, archivePrefix={arXiv}, primaryClass={cs.CV}, }